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Burger's medicinal chemistry and drug discovery
Manfred E. Wolff,William Foye,Robert Southgate,Neal Frederick Osborne,Michael J. Pearson,George Burton,SmithKline Beecham +6 more
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The article was published on 2007-01-01 and is currently open access. It has received 1671 citations till now. The article focuses on the topics: Drug discovery.read more
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Natural products: a continuing source of novel drug leads.
Gordon M. Cragg,David J. Newman +1 more
TL;DR: This review traces natural products drug discovery, outlining important drugs from natural sources that revolutionized treatment of serious diseases and effective drug development depends on multidisciplinary collaborations.
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A historical overview of natural products in drug discovery.
TL;DR: A review of historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication techniques to rapidly facilitate their discovery, and a discussion of how natural product chemistry has resulted in the identification of many drug candidates are highlighted.
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Pharmaceutical cocrystals: along the path to improved medicines.
TL;DR: Seven recent case studies that illustrate how pharmaceutical cocrystals can improve physicochemical properties and clinical performance of drug substances, including a recently approved drug product based upon an ICC, are presented.
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On Some Aspects of Variable Selection for Partial Least Squares Regression Models
Partha Pratim Roy,Kunal Roy +1 more
TL;DR: In this article, the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data is explored, where the compounds of the dataset were classified using K-means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters.
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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Katja Hansen,Grégoire Montavon,Franziska Biegler,Siamac Fazli,Matthias Rupp,Matthias Scheffler,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Klaus-Robert Müller +8 more
TL;DR: A number of established machine learning techniques are outlined and the influence of the molecular representation on the methods performance is investigated, finding the best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules.